Computational Biology Program, Oregon Health and Science University, Portland, OR, USA.
Portland VA Research Foundation, Portland, OR, USA.
BMC Cancer. 2018 Apr 13;18(1):414. doi: 10.1186/s12885-018-4325-6.
Tumor neoantigens are drivers of cancer immunotherapy response; however, current prediction tools produce many candidates requiring further prioritization. Additional filtration criteria and population-level understanding may assist with prioritization. Herein, we show neoepitope immunogenicity is related to measures of peptide novelty and report population-level behavior of these and other metrics.
We propose four peptide novelty metrics to refine predicted neoantigenicity: tumor vs. paired normal peptide binding affinity difference, tumor vs. paired normal peptide sequence similarity, tumor vs. closest human peptide sequence similarity, and tumor vs. closest microbial peptide sequence similarity. We apply these metrics to neoepitopes predicted from somatic missense mutations in The Cancer Genome Atlas (TCGA) and a cohort of melanoma patients, and to a group of peptides with neoepitope-specific immune response data using an extension of pVAC-Seq (Hundal et al., pVAC-Seq: a genome-guided in silico approach to identifying tumor neoantigens. Genome Med 8:11, 2016).
We show neoepitope burden varies across TCGA diseases and HLA alleles, with surprisingly low repetition of neoepitope sequences across patients or neoepitope preferences among sets of HLA alleles. Only 20.3% of predicted neoepitopes across TCGA patients displayed novel binding change based on our binding affinity difference criteria. Similarity of amino acid sequence was typically high between paired tumor-normal epitopes, but in 24.6% of cases, neoepitopes were more similar to other human peptides, or bacterial (56.8% of cases) or viral peptides (15.5% of cases), than their paired normal counterparts. Applied to peptides with neoepitope-specific immune response, a linear model incorporating neoepitope binding affinity, protein sequence similarity between neoepitopes and their closest viral peptides, and paired binding affinity difference was able to predict immunogenicity (AUROC = 0.66).
Our proposed prioritization criteria emphasize neoepitope novelty and refine patient neoepitope predictions for focus on biologically meaningful candidate neoantigens. We have demonstrated that neoepitopes should be considered not only with respect to their paired normal epitope, but to the entire human proteome, and bacterial and viral peptides, with potential implications for neoepitope immunogenicity and personalized vaccines for cancer treatment. We conclude that putative neoantigens are highly variable across individuals as a function of cancer genetics and personalized HLA repertoire, while the overall behavior of filtration criteria reflects predictable patterns.
肿瘤新生抗原是癌症免疫治疗反应的驱动因素；然而，目前的预测工具产生了许多需要进一步优先排序的候选物。额外的过滤标准和人群水平的理解可能有助于优先级排序。在此，我们展示了新表位的免疫原性与肽的新颖性测量值有关，并报告了这些和其他指标的人群水平行为。
我们提出了四种肽新颖性度量标准来完善预测的新抗原性：肿瘤与配对正常肽的结合亲和力差异、肿瘤与配对正常肽序列相似性、肿瘤与最接近人类肽序列相似性和肿瘤与最接近微生物肽序列相似性。我们将这些度量标准应用于从癌症基因组图谱 (TCGA) 的体细胞错义突变和黑色素瘤患者队列中预测的新表位，以及一组具有新表位特异性免疫反应数据的肽，使用 pVAC-Seq 的扩展 (Hundal 等人，pVAC-Seq：一种基于基因组的鉴定肿瘤新生抗原的计算方法。Genome Med 8:11, 2016)。
我们表明，新表位负担在 TCGA 疾病和 HLA 等位基因之间存在差异，患者之间的新表位序列重复率和 HLA 等位基因组之间的新表位偏好都非常低。在 TCGA 患者中，只有 20.3%的预测新表位基于我们的结合亲和力差异标准显示出新型结合变化。配对肿瘤-正常表位之间的氨基酸序列通常具有高度相似性，但在 24.6%的情况下，新表位与其他人类肽或细菌 (56.8%的情况下) 或病毒肽 (15.5%的情况下) 更相似，而不是它们的配对正常对应物。应用于具有新表位特异性免疫反应的肽，纳入新表位结合亲和力、新表位与其最接近病毒肽之间的蛋白质序列相似性以及配对结合亲和力差异的线性模型能够预测免疫原性 (AUROC=0.66)。
我们提出的优先级标准强调了新表位的新颖性，并针对具有生物学意义的候选新抗原对患者的新表位预测进行了优化。我们已经证明，新表位不仅应考虑与其配对的正常表位，还应考虑整个人类蛋白质组以及细菌和病毒肽，这可能对新表位免疫原性和癌症治疗的个性化疫苗产生影响。我们的结论是，作为癌症遗传学和个性化 HLA 库的函数，个体之间的假定新抗原具有高度变异性，而过滤标准的整体行为反映了可预测的模式。
