Abstract LB_B19: Utilizing genome-informed modeling to assess CDK4/6 inhibitor response in breast cancer patients, simulate clinical trials, and result in novel CDK4/6 inhibitor treatment for chordoma patients

Abstract

Abstract Varied therapeutic responses were observed among cancer patients receiving the same treatment regimen, highlighting the challenge of identifying patients most likely to benefit from a given therapy. Here, we present an artificial intelligence-based approach, called CDK4/6 inhibitor Response Model (CRM), to address the complexity of predicting patient responses to treatment by a certain clinical scene on CDK4/6 inhibitors (CDK4/6i). To train the CRM, we transformed the genomic data of 980 breast cancer patients from the TCGA database into activity profiles of signaling pathways (APSP) by utilizing the modified Damage Assessment of Genomic Mutations (DAGM) algorithm. A scoring model was then established by random forest algorithm to classify the HR+/HER2- and HR-/HER2- breast cancer molecular subtypes by the differential APSP features between the two, which reasonably reflected the potential role played by CDK4/6 molecules in HR+/HER2- breast cancer cells. The effectiveness of CRM was then tested in a separate local patient cohort (n = 343) in Guangdong, China. Twin in-silico clinical trials (ICT) of previously disclosed clinical trials (NCT02246621, NCT02079636, NCT03155997, NCT02513394, NCT02675231) were performed to demonstrate the potential of CRM in generating concerted results as the real-world clinical outcomes. The CRM displayed high precision in classifying HR+/HER2- and HR-/HER2- breast cancer patients in TCGA (AUC=0.9956) and local patient cohorts (AUC=0.9795). Significantly, the scores were distinct (p = 0.025) between CDK4/6i-treated patients with different responses. Breast cancer patients from different subtypes were grouped into five distinct populations based on the scores assigned by the CRM. From twin ICT, the CRM scores reflected the differential responses of patient groups to CDK4/6i-based therapies. Thus, the CRM score showed not only a robust association with clinically observed CDK4/6i responses but also heterogenetic responses across subtypes. More than half of HR+/HER2+ patients may benefit from CDK4/6i-based treatment. The CRM empowered us to conduct ICT on different types of cancer patients responding to CDK4/6i-based therapies. We have discovered 131 subtypes of tumors that should respond to CDK4/6i-based therapies. Finally, one of our IIT demonstrated that chordoma patients, the only go-to treatment option is surgery, greatly responded to CDK4/6i treatment. Citation Format: Mei Yang, Yuhan Liu, Yi-Ching Hsueh, Qiangzu Zhang, Yanhui Fan, Juntao Xu, Min Huang, Xu Li, Su Chen, Jianfei Yang, Gang Niu. Utilizing genome-informed modeling to assess CDK4/6 inhibitor response in breast cancer patients, simulate clinical trials, and result in novel CDK4/6 inhibitor treatment for chordoma patients [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr LB_B19.