为肝细胞癌武装 GPC3 CAR-T 细胞：多少才足够，下一步是什么？
Armouring GPC3 CAR T cells for hepatocellular carcinoma: how much is enough and what comes next?
CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
基于单样本基因表达状态推断算法的肿瘤相关抗原预测
英文原题：Tumor-associated antigen prediction using a single-sample gene expression state inference algorithm.
查看英文原题
Tumor-associated antigen prediction using a single-sample gene expression state inference algorithm.
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中文摘要
我们开发了一种基于贝叶斯的算法，用于推断个体样本中的基因表达状态，并将其纳入分析流程，以利用基因型-组织表达（GTEx）和癌症基因组图谱（TCGA）的RNA测序数据，在33种癌症中鉴定肿瘤相关抗原（TAA）。分析共鉴定出212种候选TAA，其中78种在涵盖7种癌症类型的独立RNA测序数据集中得到验证。蛋白质组学数据进一步支持其中18种TAA，包括10种与肝癌相关的抗原。我们预测，由这10种TAA衍生的38种肽可与最常见的HLA等位基因HLA-A02强结合。实验验证证实其中21种肽具有显著结合亲和力和免疫原性。值得注意的是，约64%的肝肿瘤表达与这21种肽相关的一种或多种TAA，因此这些TAA可作为肝癌治疗（如肽疫苗或T细胞受体〔TCR〕T细胞疗法）的有前景候选靶点。本研究凸显整合计算与实验方法发现免疫治疗TAA的优势。
展开英文摘要原文
We developed a Bayesian-based algorithm to infer gene expression states in individual samples and incorporated it into a workflow to identify tumor-associated antigens (TAAs) across 33 cancer types using RNA sequencing (RNA-seq) data from the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA).
Our analysis identified 212 candidate TAAs, with 78 validated in independent RNA-seq datasets spanning seven cancer types. Eighteen of these TAAs were further corroborated by proteomics data, including 10 linked to liver cancer.
We predicted that 38 peptides derived from these 10 TAAs would bind strongly to HLA-A02, the most common HLA allele. Experimental validation confirmed significant binding affinity and immunogenicity for 21 of these peptides.
Notably, approximately 64% of liver tumors expressed one or more TAAs associated with these 21 peptides, positioning them as promising candidates for liver cancer therapies, such as peptide vaccines or T cell receptor (TCR)-T cell treatments.
This study highlights the power of integrating computational and experimental approaches to discover TAAs for immunotherapy.
论文信息
- 作者
- Yi X、Zhao H、Hu S、Dong L、Dou Y、Li J、Gao Q、Zhang B
- 第一作者单位
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China; Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.China
- 通讯作者单位
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA. Electronic address: bing.zhang@bcm.edu.United States
- 文献类型
- 非美国政府资助研究 · 美国 NIH 资助研究
- 期刊
- Cell reports methods2024 Nov 18
- 原文标识
- PubMed 39561714 · DOI 10.1016/j.crmeth.2024.100906
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