Presentation Information
[3CTBP-08]Comprehensive microRNA profiling and function-based feature selection reveal a biomarker panel for predicting CAR-T cell exhaustion
○Noriko Nakamura1, Hyemin Seo1, Risa Hamada1, Hiromasa Kaneko2, Yuki Kagoya3, Seiichi Ohta1 (1. The University of Tokyo (Japan), 2. Meiji University (Japan), 3. Keio University (Japan))
Keywords:
CAR-T cell exhaustion,microRNA biomarker,microRNA sequencing
Chimeric antigen receptor (CAR)-T cell therapy has shown remarkable clinical success in hematological malignancies; however, CAR-T cell exhaustion induced by persistent antigen stimulation remains a major obstacle to durable therapeutic efficacy. Exhausted CAR-T cells exhibit reduced proliferation and antitumor activity, which can lead to treatment failure and disease relapse. While transcriptomic and epigenomic analyses have provided insights into CAR-T cell exhaustion, the role of microRNAs (miRNAs) in this process remains largely unexplored.
In this study, we performed the first comprehensive analysis of miRNA expression profiles associated with CAR-T cell exhaustion using an in vitro continuous antigen stimulation model. Human peripheral blood mononuclear cells were transduced with an anti-CD19 CAR, and repeatedly stimulated with CD19-expressing leukemia cells to induce exhaustion. Functional evaluation in a lymphoma mouse model confirmed that exhausted CAR-T cells showed reduced proliferative capacity and impaired therapeutic efficacy compared with control CAR-T cells.
To identify molecular signatures underlying the exhaustion, bulk miRNA sequencing was conducted on control and exhausted CAR-T cells. The analysis identified 39 differentially expressed miRNAs between the two groups. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of predicted target genes revealed significant associations with pathways involved in cellular proliferation, including the cell cycle, Hippo signaling pathway, and p53 signaling pathway, consistent with the impaired proliferative capacity observed in exhausted CAR-T cells.
To validate these findings, RT-qPCR analysis was performed for the 39 candidate miRNAs, resulting in 18 miRNAs showing significant expression differences between control and exhausted CAR-T cells. Because large biomarker panels are difficult to implement in clinical workflows, we further developed a function-based feature selection strategy to minimize KEGG pathway redundancy. This approach reduced the candidate set to a six-miRNA panel (miR-125a-5p, miR-128-1-5p, miR-195-3p, miR-3691-5p, miR-7974, and miR-942-3p). Machine learning models using random forest classifiers were constructed to evaluate the predictive performance of these miRNAs. The six-miRNA panel achieved a prediction accuracy of AUC = 0.89, outperforming models based on larger miRNA sets.
These findings reveal a miRNA molecular landscape associated with CAR-T cell exhaustion and demonstrate that integrative analysis combining miRNA sequencing, RT-qPCR validation, and biological function-based feature selection can identify compact and informative biomarker panels. The identified six-miRNA signature can be measured using clinically compatible RT-qPCR assays, suggesting potential applications in CAR-T cell quality control and therapeutic optimization.
In this study, we performed the first comprehensive analysis of miRNA expression profiles associated with CAR-T cell exhaustion using an in vitro continuous antigen stimulation model. Human peripheral blood mononuclear cells were transduced with an anti-CD19 CAR, and repeatedly stimulated with CD19-expressing leukemia cells to induce exhaustion. Functional evaluation in a lymphoma mouse model confirmed that exhausted CAR-T cells showed reduced proliferative capacity and impaired therapeutic efficacy compared with control CAR-T cells.
To identify molecular signatures underlying the exhaustion, bulk miRNA sequencing was conducted on control and exhausted CAR-T cells. The analysis identified 39 differentially expressed miRNAs between the two groups. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of predicted target genes revealed significant associations with pathways involved in cellular proliferation, including the cell cycle, Hippo signaling pathway, and p53 signaling pathway, consistent with the impaired proliferative capacity observed in exhausted CAR-T cells.
To validate these findings, RT-qPCR analysis was performed for the 39 candidate miRNAs, resulting in 18 miRNAs showing significant expression differences between control and exhausted CAR-T cells. Because large biomarker panels are difficult to implement in clinical workflows, we further developed a function-based feature selection strategy to minimize KEGG pathway redundancy. This approach reduced the candidate set to a six-miRNA panel (miR-125a-5p, miR-128-1-5p, miR-195-3p, miR-3691-5p, miR-7974, and miR-942-3p). Machine learning models using random forest classifiers were constructed to evaluate the predictive performance of these miRNAs. The six-miRNA panel achieved a prediction accuracy of AUC = 0.89, outperforming models based on larger miRNA sets.
These findings reveal a miRNA molecular landscape associated with CAR-T cell exhaustion and demonstrate that integrative analysis combining miRNA sequencing, RT-qPCR validation, and biological function-based feature selection can identify compact and informative biomarker panels. The identified six-miRNA signature can be measured using clinically compatible RT-qPCR assays, suggesting potential applications in CAR-T cell quality control and therapeutic optimization.
Comment
To browse or post comments, you must log in.Log in