Chai Shengjie, Smith Christof C, Kochar Tavleen K, Hunsucker Sally A, Beck Wolfgang, Olsen Kelly S, Vensko Steven, Glish Gary L, Armistead Paul M, Prins Jan F, Vincent Benjamin G
Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27514, USA.
Curriculum in Bioinformatics and Computational Biology, UNC School of Medicine, Chapel Hill, NC, 27599, USA.
Bioinform Adv. 2022 May 6;2(1):vbac032. doi: 10.1093/bioadv/vbac032. eCollection 2022.
Splice variant neoantigens are a potential source of tumor-specific antigen (TSA) that are shared between patients in a variety of cancers, including acute myeloid leukemia. Current tools for genomic prediction of splice variant neoantigens demonstrate promise. However, many tools have not been well validated with simulated and/or wet lab approaches, with no studies published that have presented a targeted immunopeptidome mass spectrometry approach designed specifically for identification of predicted splice variant neoantigens.
In this study, we describe NeoSplice, a novel computational method for splice variant neoantigen prediction based on (i) prediction of tumor-specific k-mers from RNA-seq data, (ii) alignment of differentially expressed k-mers to the splice graph and (iii) inference of the variant transcript with MHC binding prediction. NeoSplice demonstrates high sensitivity and precision (>80% on average across all splice variant classes) through simulated RNA-seq data. Through mass spectrometry analysis of the immunopeptidome of the K562.A2 cell line compared against a synthetic peptide reference of predicted splice variant neoantigens, we validated 4 of 37 predicted antigens corresponding to 3 of 17 unique splice junctions. Lastly, we provide a comparison of NeoSplice against other splice variant prediction tools described in the literature. NeoSplice provides a well-validated platform for prediction of TSA vaccine targets for future cancer antigen vaccine studies to evaluate the clinical efficacy of splice variant neoantigens.
https://github.com/Benjamin-Vincent-Lab/NeoSplice.
Supplementary data are available at online.
剪接变体新抗原是肿瘤特异性抗原（TSA）的潜在来源，在包括急性髓系白血病在内的多种癌症患者中具有共享性。目前用于剪接变体新抗原基因组预测的工具显示出了前景。然而，许多工具尚未通过模拟和/或湿实验室方法得到充分验证，且尚未有研究发表专门针对鉴定预测的剪接变体新抗原设计的靶向免疫肽组质谱方法。
在本研究中，我们描述了NeoSplice，这是一种用于剪接变体新抗原预测的新型计算方法，其基于：（i）从RNA测序数据预测肿瘤特异性k - 元组；（ii）将差异表达的k - 元组与剪接图比对；以及（iii）通过MHC结合预测推断变体转录本。通过模拟RNA测序数据，NeoSplice展示了高灵敏度和高精度（在所有剪接变体类别中平均>80%）。通过对K562.A2细胞系的免疫肽组进行质谱分析，并与预测的剪接变体新抗原的合成肽参考进行比较，我们验证了37个预测抗原中的4个，它们对应于17个独特剪接位点中的3个。最后，我们将NeoSplice与文献中描述的其他剪接变体预测工具进行了比较。NeoSplice为预测TSA疫苗靶点提供了一个经过充分验证的平台，用于未来癌症抗原疫苗研究，以评估剪接变体新抗原的临床疗效。
https://github.com/Benjamin-Vincent-Lab/NeoSplice。
补充数据可在网上获取。
