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通过反向疫苗学和生物信息学方法开发一种针对非小细胞肺癌的多新抗原疫苗。

Development of a multi-neoepitope vaccine targeting non-small cell lung cancer through reverse vaccinology and bioinformatics approaches.

作者信息

Asadollahi Elahe, Zomorodipour Alireza, Soheili Zahra-Soheila, Jahangiri Babak, Sadeghizadeh Majid

机构信息

Department of Molecular Genetics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran.

Department of Molecular Medicine, Institute of Medical Biotechnology, Institute of Genetic Engineering and Biotechnology, Tehran, Iran.

出版信息

Front Immunol. 2025 May 16;16:1521700. doi: 10.3389/fimmu.2025.1521700. eCollection 2025.

Abstract

INTRODUCTION

Lung cancer, predominantly non-small cell lung cancer (NSCLC), is the leading cause of cancer-related mortality worldwide. Among immunotherapeutic strategies, the personalized multi-neoepitope vaccine (MNEV) offers a promising approach for managing advanced-stage NSCLC.

METHODS

We used reverse vaccinology, immunoinformatics, and bioinformatics to design an MNEV targeting lung cancer in murine (LL/2) cells. Whole exome sequencing (WES) and RNA sequencing data from human and mouse NSCLC cell lines were analyzed to select neoantigens, which were evaluated for their ability to stimulate B cells, helper T lymphocytes (HTLs), and cytotoxic T lymphocytes (CTLs). Molecular docking studies estimated the binding affinity of mouse neoepitopes with MHC class I, MHC class II, and B-cell receptors. Suitable linkers were selected to construct the MNEV, with the 50S L7/L12 ribosomal protein sequence included as an adjuvant to enhance immune responses. The immunoglobulin kappa (Igκ) chain signal peptide was incorporated to improve secretion efficiency. The stability of the final MNEV construct in complex with TLR3, TLR4, and TLR9 was confirmed through binding analysis and refinement of the best-predicted 3D model. To evaluate the immunological efficacy of the MNEV, female C57BL/6 mice were immunized subcutaneously. Immune responses were assessed by measuring total IgG levels in serum using enzyme-linked immunosorbent assay (ELISA) and quantifying IFN-γ and granzyme B levels in the supernatant of cultured splenocytes. The proportions of CD19+ B cells and CD4+ and CD8+ T cells were determined using flow cytometric analysis.

RESULTS

In silico evaluations indicated that the MNEV is non-toxic, non-allergenic, and stable, exhibiting high-affinity interactions with B lymphocytes, CTLs, and HTLs. Immunization with the MNEV significantly increased serum IgG levels. Flow cytometry analysis revealed higher percentages of CD19+ B cells and CD4+ and CD8+ T cells. Furthermore, splenocytes from immunized mice showed a marked increase in IFN-γ and granzyme B secretion compared to control groups.

DISCUSSION

This study demonstrates that the MNEV induces a robust strong immune response, highlighting its potential as a promising approach for cancer prevention and immunotherapy, particularly for NSCLC. Furthermore, it provides a foundation for developing neoepitope-based vaccines against various malignancies, guiding future research in cancer vaccine development through advanced computational methods in immunology and oncology.

摘要

引言

肺癌,主要是非小细胞肺癌(NSCLC),是全球癌症相关死亡的主要原因。在免疫治疗策略中,个性化多新抗原疫苗(MNEV)为晚期NSCLC的治疗提供了一种有前景的方法。

方法

我们利用反向疫苗学、免疫信息学和生物信息学在小鼠(LL/2)细胞中设计了一种针对肺癌的MNEV。分析来自人和小鼠NSCLC细胞系的全外显子测序(WES)和RNA测序数据以选择新抗原,并评估它们刺激B细胞、辅助性T淋巴细胞(HTL)和细胞毒性T淋巴细胞(CTL)的能力。分子对接研究估计了小鼠新抗原与MHC I类、MHC II类和B细胞受体的结合亲和力。选择合适的接头构建MNEV,并纳入50S L7/L12核糖体蛋白序列作为佐剂以增强免疫反应。引入免疫球蛋白κ(Igκ)链信号肽以提高分泌效率。通过结合分析和对最佳预测三维模型的优化,证实了最终MNEV构建体与TLR3、TLR4和TLR9复合物的稳定性。为了评估MNEV的免疫效果,对雌性C57BL/6小鼠进行皮下免疫。通过酶联免疫吸附测定(ELISA)测量血清中的总IgG水平,并通过定量培养脾细胞上清液中的IFN-γ和颗粒酶B水平来评估免疫反应。使用流式细胞术分析确定CD19+B细胞以及CD4+和CD8+T细胞的比例。

结果

计算机模拟评估表明,MNEV无毒、无致敏性且稳定,与B淋巴细胞、CTL和HTL表现出高亲和力相互作用。用MNEV免疫可显著提高血清IgG水平。流式细胞术分析显示CD19+B细胞以及CD4+和CD8+T细胞的百分比更高。此外,与对照组相比,免疫小鼠的脾细胞显示IFN-γ和颗粒酶B分泌显著增加。

讨论

本研究表明,MNEV可诱导强烈的免疫反应,突出了其作为癌症预防和免疫治疗,特别是NSCLC的有前景方法的潜力。此外,它为开发针对各种恶性肿瘤的基于新抗原的疫苗提供了基础,通过免疫学和肿瘤学中的先进计算方法指导癌症疫苗开发的未来研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c446/12122770/65be792945f0/fimmu-16-1521700-g001.jpg

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