个性化癌症疫苗已成为癌症治疗或预防策略的有希望的途径。这种方法针对个体患者肿瘤中的特定遗传改变,提供更个性化和有效的治疗选择。以前的研究表明,针对有限范围的基因突变的广义肽疫苗是无效的,强调需要个性化的方法。虽然研究已经探索了个性化的mRNA疫苗,在这种情况下,尚未研究个性化的肽疫苗。胰腺导管腺癌(PDAC)在肿瘤学中仍然具有挑战性,需要创新的治疗策略。在这项研究中,我们开发了一种个性化的肽疫苗设计方法,采用RNA测序(RNAseq)鉴定患者实体瘤组织中PDAC发展的流行基因突变。我们对修剪适配器进行了RNAseq分析,读取对齐,和体细胞变异呼叫。我们还开发了一个名为SCGeneID的Python程序,这验证了RNAseq分析的比对。Python程序可以免费下载。利用染色体数和基因座数据,SCGeneID沿着UCSChg38参考集鉴定靶基因。根据基因突变数据,我们开发了一种个性化的PDAC癌症疫苗,该疫苗针对两名患者的100个高度流行的基因突变.我们预测肽-MHC结合亲和力,免疫原性,抗原性,变应原性,和每个表位的毒性。然后,我们根据之前发表的疫苗设计方法选择了前50和100个表位.最后,我们产生了pMHC-TCR三维分子模型复杂结构,可免费下载。设计的个性化癌症疫苗含有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,允许针对癌细胞的更精确和有针对性的免疫反应。此外,我们发现了突变基因,在参考研究中也发现了这一点,我们获得了测序数据,从而验证我们的疫苗设计方法。这是第一个使用人类患者数据设计针对新抗原的个性化肽癌症疫苗的研究,以鉴定与感兴趣的特定肿瘤相关的基因突变。
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.