设计靶向新抗原的个性化癌症疫苗的新方法:应用于胰腺导管腺癌

Diseases Pub Date : 2024-07-11 DOI:10.3390/diseases12070149
Kush Savsani, S. Dakshanamurthy
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引用次数: 0

摘要

个性化癌症疫苗已成为癌症治疗或预防策略的一个前景广阔的途径。这种方法针对患者肿瘤中的特定基因改变,提供了更个性化、更有效的治疗方案。以往的研究表明,针对有限范围基因突变的通用肽疫苗效果不佳,这就强调了个性化方法的必要性。虽然已有研究对个性化 mRNA 疫苗进行了探索,但在这种情况下,个性化多肽疫苗尚未得到研究。胰腺导管腺癌(PDAC)仍然是肿瘤学中的难题,需要创新的治疗策略。在本研究中,我们开发了一种个性化多肽疫苗设计方法,利用 RNA 测序(RNAseq)来确定患者实体瘤组织中 PDAC 发生的流行基因突变。我们进行了 RNAseq 分析,包括修剪适配体、读取比对和体细胞变异调用。我们还开发了一个名为 SCGeneID 的 Python 程序,用于验证 RNAseq 分析的比对结果。Python 程序可免费下载。SCGeneID 利用染色体号和基因座数据,沿着 UCSC hg38 参考集识别目标基因。根据基因突变数据,我们开发了针对两名患者 100 个高发基因突变的个性化 PDAC 癌症疫苗。我们预测了每个表位的肽-MHC 结合亲和力、免疫原性、抗原性、过敏性和毒性。然后,我们根据之前公布的疫苗设计方法,选出了前 50 个和前 100 个表位。最后,我们生成了 pMHC-TCR 三维分子模型复合物结构,可免费下载。所设计的个性化癌症疫苗包含 PDAC 实体瘤组织中常见的表位。我们的个性化疫苗由新抗原组成,可对癌细胞产生更精确、更有针对性的免疫反应。此外,我们还发现了突变基因,这些基因在我们获得测序数据的参考研究中也有发现,从而验证了我们的疫苗设计方法。这是第一项利用人类患者数据设计针对新抗原的个性化多肽癌症疫苗的研究,目的是识别与特定肿瘤相关的基因突变。
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Novel Methodology for the Design of Personalized Cancer Vaccine Targeting Neoantigens: Application to Pancreatic Ductal Adenocarcinoma
Personalized cancer vaccines have emerged as a promising avenue for cancer treatment or prevention strategies. This approach targets the specific genetic alterations in individual patient’s tumors, offering a more personalized and effective treatment option. Previous studies have shown that generalized peptide vaccines targeting a limited scope of gene mutations were ineffective, emphasizing the need for personalized approaches. While studies have explored personalized mRNA vaccines, personalized peptide vaccines have not yet been studied in this context. Pancreatic ductal adenocarcinoma (PDAC) remains challenging in oncology, necessitating innovative therapeutic strategies. In this study, we developed a personalized peptide vaccine design methodology, employing RNA sequencing (RNAseq) to identify prevalent gene mutations underlying PDAC development in a patient solid tumor tissue. We performed RNAseq analysis for trimming adapters, read alignment, and somatic variant calling. We also developed a Python program called SCGeneID, which validates the alignment of the RNAseq analysis. The Python program is freely available to download. Using chromosome number and locus data, SCGeneID identifies the target gene along the UCSC hg38 reference set. Based on the gene mutation data, we developed a personalized PDAC cancer vaccine that targeted 100 highly prevalent gene mutations in two patients. We predicted peptide-MHC binding affinity, immunogenicity, antigenicity, allergenicity, and toxicity for each epitope. Then, we selected the top 50 and 100 epitopes based on our previously published vaccine design methodology. Finally, we generated pMHC-TCR 3D molecular model complex structures, which are freely available to download. The designed personalized cancer vaccine contains epitopes commonly found in PDAC solid tumor tissue. Our personalized vaccine was composed of neoantigens, allowing for a more precise and targeted immune response against cancer cells. Additionally, we identified mutated genes, which were also found in the reference study, where we obtained the sequencing data, thus validating our vaccine design methodology. This is the first study designing a personalized peptide cancer vaccine targeting neoantigens using human patient data to identify gene mutations associated with the specific tumor of interest.
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