Biology subjects

Miura-Yamashita, T.

Publications and source records attributed to Miura-Yamashita, T..

2 recordsLinked to original sources

bioRxiv2026

Cytotoxicity-based High-throughput Screening System for CAR T Cell

Some chimeric antigen receptor (CAR) T cell therapies have shown strong clinical efficacy, yet systematic screening of new CAR designs remains constrained by labor-intensive, low-throughput evaluation methods. To address this limitation, we developed a cytotoxicity-centered, high-throughput screening platform that integrates single-cell pooled screening with fully automated arrayed screening enabling both large-scale library handling and quantitative functional resolution for systematic CAR design exploration. Using a mutation-based CAR design approach guided by protein fitness prediction, we generated a 4-1BB-based CAR library with approximately 10 theoretical variants while minimizing the prevalence of low-activity designs. In pooled screening, CAR T cells were evaluated at the single-cell level based on cytotoxicity and proliferation, enabling rapid enrichment of high-performing variants from a highly diverse library. Subsequent automated arrayed screening quantitatively measured cytotoxicity with high reproducibility, providing high-resolution functional data suitable for comparative ranking. Selected CAR variants demonstrated superior antitumor efficacy in a leukemia xenograft model compared with a template CAR. Furthermore, systematic analysis of mutation sites from an enhanced CAR variant identified essential mutation combinations underlying functional enhancement. Together, this study establishes a cytotoxicity-focused screening framework that provides a robust approach for optimizing CAR architectures and accelerating the development of CAR T-cell therapies.

synthetic biology↗

bioRxiv2025

Enhancing CAR-T cell activity prediction via fine-tuning protein language models with generated CAR sequences

Chimeric antigen receptor (CAR)-T cell therapy has shown remarkable success in treating hematological malignancies; however, several challenges remain, including limited efficacy against solid tumors, T cell exhaustion, and lack of T cell persistence, which have restricted its clinical efficacy across various indications. Sequence optimization of CAR constructs offers a promising strategy to enhance therapeutic efficacy of CAR-T cells. Recent advances in machine learning, especially protein language models (PLMs), enable prediction of mutational effects based on sequence representations. Nevertheless, applying PLMs to CARs is challenging due to the artificial nature of CARs and the absence of comprehensive CAR sequence databases. In this study, we developed a computational framework to predict CAR-T cell activity by fine-tuning ESM-2 with the CAR sequences generated using sequence augmentation. These CAR sequences were constructed by recombining homologous domains of CARs, enabling task-specific adaptation of the model. To evaluate prediction performance, we experimentally assessed the cytotoxicity of CAR-T cells expressing mutated CAR variants and compared these results with model predictions. Our results demonstrated that fine-tuned ESM-2 significantly improves prediction performance of CAR-T cell activity. Furthermore, we showed that training parameters--such as sequence diversity, number of training steps, and model size--substantially influence prediction performance. This work highlights the potential of combining sequence augmentation with fine-tuning PLMs to advance data-driven CAR-T cell design.

bioinformatics↗