Abstract 3020: Machine learning-based cluster analysis of immune cell subtypes and breast cancer survival

Abstract Background: Host immunity involves various immune cells working in concert to achieve balanced immune response. Host immunity interacts with tumorigenic process impacting disease outcome. Clusters of different immune cells may indicate specific host-tumor interplay. Identifying the clusters may reveal unique host immunity in response to tumor growth. Methods: CIBERSORT was used to estimate relative abundances of 22 immune cell types in 3 datasets, METABRIC, TCGA, and our study. The cell type data in METABRIC were analyzed for cluster using unsupervised hierarchical clustering (UHC). The UHC results were employed to train machine learning models, random forest (RF), deep neural network (DNN), stepAIC, and elastic net. Kaplan-Meier and Cox regression survival analyses were performed to assess cell clusters in association with relapse-free and overall survival. Differentially expressed genes (DEGs) by immune cell clusters were interrogated with IPA for molecular signatures. Results: UHC analysis identified two distinct immune cell clusters, clusters A (83.2%) and B (16.8%). Memory B cells, plasma cells, CD8 positive T cells, resting memory CD4 T cells, activated NK cells, monocytes, M1 macrophages, and resting mast cells were more abundant in clusters A than B, whereas regulatory T cells and M0 and M2 macrophages were more in clusters B than A. Patients in cluster A had favorable survival compared to those in cluster B. Similar survival associations were also observed in TCGA and our study when using a RF model trained with the UHC results. The survival associations were independent from clinicopathological variables. IPA analysis showed that pathogen-induced cytokine storm signaling pathway, phagosome formation, and T cell receptor signaling were related to the cell type clusters. Conclusions: Our finding suggests that different immune cell clusters may indicate distinct immune responses to tumor growth, suggesting their potential for disease management. Citation Format: Zhanwei Wang, Dionyssios Katsaros, Junlong Wang, Nicholetta Biglio, Brenda Y. Hernandez, Peiwen Fei, Lingeng Lu, Harvey Risch, Herbert Yu. Machine learning-based cluster analysis of immune cell subtypes and breast cancer survival [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3020.