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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-γdominant)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.本文利用蛋白质组学数据对基因组数据计算预测得到的候选新抗原进一步筛选,提高了新抗原预测准确性,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. 相似文献
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Nikolai Slavov 《Molecular & cellular proteomics : MCP》2022,21(1):100179
Single-cell tandem MS has enabled analyzing hundreds of single cells per day and quantifying thousands of proteins across the cells. The broad dissemination of these capabilities can empower the dissection of pathophysiological mechanisms in heterogeneous tissues. Key requirements for achieving this goal include robust protocols performed on widely accessible hardware, robust quality controls, community standards, and automated data analysis pipelines that can pinpoint analytical problems and facilitate their timely resolution. Toward meeting these requirements, this perspective outlines both existing resources and outstanding opportunities, such as parallelization, for catalyzing the wide dissemination of quantitative single-cell proteomics analysis that can be scaled up to tens of thousands of single cells. Indeed, simultaneous parallelization of the analysis of peptides and single cells is a promising approach for multiplicative increase in the speed of performing deep and quantitative single-cell proteomics. The community is ready to begin a virtuous cycle of increased adoption fueling the development of more technology and resources for single-cell proteomics that in turn drive broader adoption, scientific discoveries, and clinical applications. 相似文献
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