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Establishment of PBMC-Derived Humanized Breast Cancer Mouse Model for Exploring the Effectiveness of PD-1 Targeted Immunotherapy
Vol 37, Issue 12, 2023
Abstract
Background: Among the limited therapeutic strategies for late-stage or triple-negative breast cancer (TNBC), immunotherapy has become an available method to prolong lifespan. Therefore, we aimed to explore immunotherapy response markers in humanized mice models to identify sub-populations that respond positively to immunotherapy. Method: A mixture of peripheral blood mononuclear cells (PBMCs) and human breast cancer cells MDA-MB-231 was injected into mice to develop a PBMC cell line-derived xenograft (CDX) mouse model. The mice were treated with pembrolizumab, a programmed cell death protein-1 (PD-1) specific antibody. The tumor growth was monitored and recorded, and tumor volume was calculated using caliper measurements. The immunophenotyping was carried out using flow cytometry. The screening of differentially expressed genes and functional enrichment analysis were done through RNAseq and bioinformatics. Results: A gradual decrease in tumor size was observed on day 7, day 10, and day 14 within the PD-1 treated group compared to the control group, although no statistical significance was observed (p > 0.05). Compared to the control group, RNAseq analysis on day 14 of the PD-1 treated group revealed nine genes with significant changes, including one upregulated gene and eight downregulated genes. Using the GSE124821 dataset, it was found that Nucleoporin 210 kDa (NUP210) was downregulated in both studies (p < 0.05) and proved to be responsive to immunotherapy in the validation dataset (GSE169246). Additionally, pathway analysis revealed that NUP210 is crucial in an immune response process. Conclusions: Our study demonstrated that pembrolizumab could control tumors driven by the MDA-MB-231 cell line. Additionally, we identified NUP210 as a potential responsive marker for immunotherapy of pembrolizumab in breast cancer. These findings may provide significant insights into the future of breast cancer treatment.
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Copyright (c) 2023 Jing-jing Chen, Peng Shi, Yu-feng Li, Jie Ma, Zhi-chao Cui, Jing-hua Zhang
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Medical Genetics, University of Torino Medical School, Italy

Department of Biomedical, Surgical and Dental Sciences, University of Milan, Italy