李雨雨,王广志,陈兰明,谢鹭.基于蛋白质基因组学方法的新抗原鉴定流程[J].生物化学与生物物理进展(待发表)
基于蛋白质基因组学方法的新抗原鉴定流程
The workflow of neoantigen identification based on proteogenomic methodology
投稿时间:2018-11-24  修订日期:2019-05-17
中文关键词:  蛋白质基因组学 新抗原 乳腺癌
英文关键词:proteogenomic, neoantigen, breast cancer
基金项目:国家自然科学基金(31870829)
作者单位E-mail
李雨雨 上海海洋大学食品科学与技术学院 18396814284@163.com 
王广志 上海海洋大学食品科学与技术学院  
陈兰明 上海海洋大学食品科学与技术学院 lmchen@shou.edu.cn 
谢鹭 上海生物信息技术研究中心(录属于上海科学院) luxiex2017@outlook.com 
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中文摘要:
      肿瘤新抗原是免疫治疗的重要靶点,但基因组数据产生的候选新抗原数量庞大,预测假阳性肽段过多,实验验证费时费力,影响肿瘤新抗原的临床应用. 本研究以乳腺癌为例,使用比转录组水平筛选更严格、比细胞学实验更省时的蛋白质基因组学方法来预测和筛选新抗原. 研究发现,C2 (IFN-γ dominant) 免疫表型的新抗原数量最多. C2免疫表型显示出最高的M1/M2巨噬细胞极化,较强的CD8信号和最大的T细胞受体多样性,这可能导致产生更多而且具有良好免疫原性的新抗原. 另外,我们还观察到乳腺癌肿瘤突变负荷与新抗原数目之间呈正相关. 通过同批样本的质谱数据进一步筛选发现可将两万级别的预测新抗原肽候选降至几十条表达肽段,进而可分析其对应的特异或广谱突变基因. 最后,我们进一步分析了新抗原的免疫原性,即被T细胞受体识别的可能性. 本文利用蛋白组学数据对基因组数据计算预测得到的候选新抗原进行进一步筛选,提高了新抗原预测准确性,可大大缩小后续实验验证范围. 该流程可为肿瘤新抗原预测与筛选研究提供参考.
英文摘要:
      Tumor neoantigens are important targets for immunotherapy. Based on high-throughput tumor genomic analysis, each missense mutation can potentially give rise to multiple neopeptides, numerous false positive neoantigens will be produced, experimental verification would require immense time and experience. Specific identification of immunogenic candidate neoantigens is consequently a major challenge. Here we introduce a workflow to predict and filter neoantigens of breast cancer with proteogenomic methodology, which can be beneficial to high quality identification of neoantigens. We found that C2 (IFN-γdominant) immunophenotype possessed the most number of neoantigens. C2 immunophenotype shows the highest M1/M2 macrophage polarization, a strong CD8 signal and the greatest T cell receptor (TCR) diversity, which may lead to more neoantigen number than in other immunophenotypes of breast cancer. In addition, we also observed that there is a positive linear relationship between neoantigen number and tumor mutation burden. By further screening for predicted tumor mutant peptides using mass spectral data of breast cancer, we found that more than 20,000 predicted neoantigens were reduced to dozens of mutant peptides in protein expression level, the corresponding mutant genes could be further analyzed. Finally, in order to define which neoantigens were more likely to be immunogenic, TCR recognition probability was calculated using blastp method. In this study, proteomics data was used to further screen the predicted neoantigens, which improved the prediction accuracy of neoantigens, and could greatly reduce the validation scope of potential subsequent experiments. This workflow provides a new insight for tumor neoantigen prediction and screening.
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