从体细胞突变中预测肿瘤特异性剪接,作为新抗原候选源。

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Bioinformatics advances Pub Date : 2024-05-29 eCollection Date: 2024-01-01 DOI:10.1093/bioadv/vbae080
Franziska Lang, Patrick Sorn, Martin Suchan, Alina Henrich, Christian Albrecht, Nina Köhl, Aline Beicht, Pablo Riesgo-Ferreiro, Christoph Holtsträter, Barbara Schrörs, David Weber, Martin Löwer, Ugur Sahin, Jonas Ibn-Salem
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引用次数: 0

摘要

动机:新抗原是很有希望的癌症免疫疗法靶点,可能来自于替代剪接。然而,检测肿瘤特异性剪接具有挑战性,因为在肿瘤中发现的许多非经典剪接接头也出现在健康组织中。为了提高肿瘤特异性,我们重点研究了体细胞突变引起的剪接,以此作为个体患者新抗原候选物的来源:我们开发了具有多种功能的工具 splice2neo,将体细胞突变预测的剪接效应与肿瘤 RNA-seq 中检测到的剪接接头整合在一起,并对由此产生的转录本和肽序列进行注释。此外,我们还提供了 EasyQuant 工具,用于将定向 RNA-seq 读数映射到候选剪接接头。利用严格的检测规则,我们在黑色素瘤队列中为每位患者预测了 1.7 个剪接接头作为剪接目标,错误发现率低于 5%。我们使用独立的健康组织样本证实了肿瘤特异性。此外,我们还利用肿瘤衍生 RNA 通过实验证实了单个外显子跳接事件。大多数目标剪接接头编码的新表位候选基因与主要组织相容性复合体(MHC)-I或MHC-II结合。与来自非同义点突变的新表位候选肽相比,剪接衍生的 MHC-I 新表位候选肽与相应野生型肽的自相似性较低。总之,我们证明了识别突变衍生的肿瘤特异性剪接接头可以产生更多的新抗原候选,从而扩大癌症免疫疗法的靶标范围:R软件包splice2neo和python软件包EasyQuant分别可在https://github.com/TRON-Bioinformatics/splice2neo 和https://github.com/TRON-Bioinformatics/easyquant。
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Prediction of tumor-specific splicing from somatic mutations as a source of neoantigen candidates.

Motivation: Neoantigens are promising targets for cancer immunotherapies and might arise from alternative splicing. However, detecting tumor-specific splicing is challenging because many non-canonical splice junctions identified in tumors also appear in healthy tissues. To increase tumor-specificity, we focused on splicing caused by somatic mutations as a source for neoantigen candidates in individual patients.

Results: We developed the tool splice2neo with multiple functionalities to integrate predicted splice effects from somatic mutations with splice junctions detected in tumor RNA-seq and to annotate the resulting transcript and peptide sequences. Additionally, we provide the tool EasyQuant for targeted RNA-seq read mapping to candidate splice junctions. Using a stringent detection rule, we predicted 1.7 splice junctions per patient as splice targets with a false discovery rate below 5% in a melanoma cohort. We confirmed tumor-specificity using independent, healthy tissue samples. Furthermore, using tumor-derived RNA, we confirmed individual exon-skipping events experimentally. Most target splice junctions encoded neoepitope candidates with predicted major histocompatibility complex (MHC)-I or MHC-II binding. Compared to neoepitope candidates from non-synonymous point mutations, the splicing-derived MHC-I neoepitope candidates had lower self-similarity to corresponding wild-type peptides. In conclusion, we demonstrate that identifying mutation-derived, tumor-specific splice junctions can lead to additional neoantigen candidates to expand the target repertoire for cancer immunotherapies.

Availability and implementation: The R package splice2neo and the python package EasyQuant are available at https://github.com/TRON-Bioinformatics/splice2neo and https://github.com/TRON-Bioinformatics/easyquant, respectively.

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