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通过RAFT使用LENS工作流程预测肿瘤抗原。

Predicting Tumor Antigens Using the LENS Workflow Through RAFT.

作者信息

Vensko Ii Steven P, Bortone Dante, Vincent Benjamin G

机构信息

Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

出版信息

Methods Mol Biol. 2025;2932:319-342. doi: 10.1007/978-1-0716-4566-6_18.

Abstract

Tumor-specific and tumor-associated antigens presented on the tumor cell surface by MHC molecules are enticing targets for personalized vaccination and T cell receptor-engineered T cell (TCR-T) therapy. Accurately predicting suitable tumor antigens is a considerable challenge and requires flexibility in both computational tools and experimental methods. Here we describe our framework for reproducible bioinformatics, RAFT, as well as our highly modular neoantigen prediction workflow, LENS. We provide step-by-step instructions for installation, running, and modifying LENS to suit different purposes.

摘要

由MHC分子呈递在肿瘤细胞表面的肿瘤特异性和肿瘤相关抗原,是个性化疫苗接种和T细胞受体工程化T细胞(TCR-T)疗法诱人的靶点。准确预测合适的肿瘤抗原是一项重大挑战,需要计算工具和实验方法具备灵活性。在这里,我们描述了我们可重复生物信息学的框架RAFT,以及我们高度模块化的新抗原预测工作流程LENS。我们提供了安装、运行和修改LENS以适应不同目的的分步说明。

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