PO.BCS01.14 · 生物信息与计算
NeonDisco：一个用于对复发性新抗原候选物进行计算机模拟发现与优先排序的 Nextflow 编排框架
NeonDisco: A Nextflow orchestration framework for in silico discovery and prioritization of recurrent neoantigen candidates
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
基于新抗原的癌症免疫治疗需要发现能够在患者中引发临床可观察反应的肿瘤特异性抗原。生物信息学的进步已经实现了新抗原的计算机模拟预测，但癌症疫苗开发一直受限于免疫原性新抗原的低发现率。这主要是由于预测新抗原的搜索空间受限，即单核苷酸变异（SNV）或插入-缺失突变（indel），而这些变异的流行程度在不同癌症类型中也各不相同。因此，将新抗原图景扩展到 SNV/indel 之外势在必行，这既是为了改进新型新抗原的检测，也是为了使通用的、现成的癌症疫苗开发成为可能。我们在 Nextflow 中实现了 NeonDisco，一个多模态生物信息学流程，以促进从 RNA-seq 数据中发现复发性、免疫原性新抗原。该流程整合了最先进的生物信息学工具，并注重模块化，以支持从扩展的新抗原来源进行预测。我们在 NeonDisco 中构建了一个基因融合新抗原发现模块的原型，并分析了来自马来西亚乳腺癌队列 MyBrCa 的 990 例患者样本的 RNA-seq 文库。我们识别出 96 个复发性融合断点，这些断点见于 886 个融合阳性样本中的 208 个独特样本，相当于 23% 的队列覆盖率。流程中的新肽预测部分识别出 2,827 个独特的新表位序列，预测其对 MyBrCa 队列中 5% 最常见 MHC-I 等位基因的 IC50 <500 nM。未来工作将侧重于将发现模块扩展到源自可变剪接和 RNA 编辑的新抗原搜索空间，以及在体外验证基因融合预测的入围名单。NeonDisco 力图将创新的新抗原发现算法整合到一个精简、自动化的流程中，这一努力构成了我们开发现成癌症疫苗优化策略的第一步。
查看英文原文 English abstract
Neoantigen-based cancer immunotherapy necessitates discovery of tumor-specific antigens that elicit clinically observable response in patients. Advances in bioinformatics have allowed in silico predictions of neoantigens, yet cancer vaccine development have been limited by low discovery rate of immunogenic neoantigens. This is primarily due to the restricted search space from which neoantigens are predicted, namely single-nucleotide variations (SNVs) or insertion-deletion mutations (indels), the prevalence of which also varies across different cancer types. Therefore, expanding the neoantigen landscape beyond SNVs/indels is imperative, both to improve novel neoantigen detection and to make universal, off-the-shelf cancer vaccine development possible. We have implemented NeonDisco, a multimodal bioinformatics pipeline in Nextflow to facilitate discovery of recurrent, immunogenic neoantigens from RNA-seq data. The pipeline incorporates state-of-the-art bioinformatics tools, with a focus on modularity to enable predictions from expanded neoantigen sources. We have prototyped a gene fusion neoantigen discovery module in NeonDisco and analyzed RNA-seq libraries of 990 patient samples from the Malaysian breast cancer cohort, MyBrCa. We identified 96 recurrent fusion breakpoints found across 208 unique samples out of 886 fusion-positive samples, translating to a cohort coverage of 23%. Neopeptide prediction part of the pipeline identified 2,827 unique neoepitope sequences predicted to have IC50 <500 nM to 5% top common MHC-I alleles in the MyBrCa cohort. Future works would focus on extending the discovery modules into alternative-splicing-derived and RNA-editing-derived neoantigen search space, as well as validating gene fusion prediction shortlist in vitro. NeonDisco attempts to consolidate innovative neoantigen discovery algorithms into one streamlined, automated pipeline, and this effort constitute our first step in an optimized strategy to develop off-the-shelf cancer vaccines.
利益披露 Disclosure
M. Azizan, None..
M. Tan, None..
J. Pan, None.