Genome-wide mapping and characterization of exitrons in human cancer

人类癌症中激子的全基因组图谱和表征

• 批准号: 10362364
• 负责人: Rendong Yang
• 金额: $ 36.6万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10362364
• 关键词: Affect; Algorithms; Alternative Splicing; Automobile Driving; Basic Science; Benchmarking; Bioinformatics; Cancer Diagnostics; Cancer Vaccine Related Development; Cancer Vaccines; Clinical; Code; Computational algorithm; Computing Methodologies; Custom; DNA Sequence Alteration; Data; Detection; Development; Diagnostic; Event; Exons; Genes; Genetic; Goals; Human; Immune Targeting; Immune checkpoint inhibitor; Introns; Joints; Knowledge; Link; Machine Learning; Malignant Neoplasms; Malignant neoplasm of prostate; Mass Spectrum Analysis; Mediator of activation protein; Meta-Analysis; Molecular; Multiomic Data; Mutate; Mutation; Neoplasm Metastasis; Nonsense-Mediated Decay; Oncogenes; Outcome; Pathway interactions; Patients; Pattern; Post-Translational Modification Site; Protein Isoforms; Proteins; Proteomics; RNA; RNA Degradation; RNA Splicing; Reading Frames; Regulation; Research; Resistance; Role; Sample Size; Sampling; Sensitivity and Specificity; Site; Source; T cell therapy; T-Lymphocyte; Technology; Testing; The Cancer Genome Atlas; Therapeutic; Translating; Tumor Suppressor Genes; Tumor-Derived; Variant; Work; base; cancer immunotherapy; cancer initiation; cancer therapy; cancer type; checkpoint inhibition; checkpoint therapy; clinical biomarkers; clinically relevant; computational platform; computerized tools; exome sequencing; exon skipping; experimental study; genetic immunotherapy; genome-wide; immune checkpoint blockade; immunogenic; innovation; insight; large datasets; loss of function; machine learning model; malignant breast neoplasm; multiple omics; neoantigens; non-genetic; novel; novel strategies; patient response; peptide I; predicting response; prognostic; protein function; response; sequencing platform; simulation; supervised learning; targeted treatment; transcriptome; transcriptome sequencing; transcriptomics; tumor; tumor progression; tumorigenesis

Project Summary & Abstract Advance in sequencing technology and computational algorithms revealed various alternative splicing variations in cancer transcriptome. Although several common classes of splicing events, such as exon skipping, intron retention and alternative splice sites, have been linked to tumor progression and therapy resistance, the roles of many non-canonical splicing events in cancer remain unknown due to the lack of dedicated approaches to detect and characterize these events. This proposal will focus on exitron splicing events because emerging evidence revealed they are dysregulated in cancer and occurred frequently in cancer-related genes. An exitron is an internal region within a coding exon that has splicing potential to create a cryptic intron. Splicing of exitron results in protein isoforms with altered sequences that may affect functional domains and post-translational modification sites. The observations of exitron splicing occurred in cancer genes suggest that exitron-spliced isoforms may contribute to cancer development. Furthermore, tumor- specific exitron splicing junctions resulting internal deletions or frameshifts may generate immunogenic peptides (i.e., neoantigens) that could form a basis for developing cancer vaccines or T-cell therapeutic targets. In this proposal, we will develop customized computational methods and conduct integrative multi- omics analysis with the goal to uncover the regulation of exitron splicing, driver exitron splicing events and neoantigens derived from tumor-specific exitrons in cancers. (Aim 1) We will develop an integrated framework to detect and validate exitrons with joint analysis of multi-omics data generated by multiple sequencing platforms. We will identify splicing factors that preferentially affect exitron splicing in cancers. (Aim 2) We will develop novel statistical approaches to identify genes and pathways enriched with exitron splicing alterations. We will implement a semi-supervised machine learning model to predict exitron splicing-associated cancer driver genes based on transcriptomic features. (Aim 3) We will develop a computational tool to identify splicing- derived neoantigens and validate them through mass spectrometry-based immunopeptidome data. We will assess the association of exitron splicing-derived neoantigens with clinical outcomes in patients receiving immune checkpoint inhibitor therapy. This project will provide a unique computational platform for dedicated exitron splicing analyses. The knowledge gained from this proposed study will help to understand the underlying mechanisms by which exitron alterations promote cancer progression. We expect that these analyses will be rapidly translated into clinical utility by providing new approaches to predict patient response in immune checkpoint inhibition therapies.

项目摘要和摘要 测序技术和计算算法的进步揭示了各种替代剪接 癌症转录组的变化。虽然几个常见的剪接事件，例如外显子 跳过，内含子保留和替代剪接位点已与肿瘤进展和治疗有关 抗性，由于缺乏 专门检测和表征这些事件的方法。该建议将重点放在出口拼接上 事件是因为新出现的证据表明它们在癌症中失调，并且经常发生在 与癌症相关的基因。出口是编码外显子中具有剪接潜力的内部区域 一个神秘的内含子。出口的剪接导致蛋白质同工型具有改变的序列，可能会影响功能 域和翻译后修改站点。癌症发生的出脱粒剪接的观察结果 基因表明，放大的同工型可能有助于癌症的发展。此外，肿瘤 - 特定的出口剪接连接处产生的内部缺失或移交可能会产生免疫原性 肽（即新抗原），可以形成开发癌症疫苗或T细胞治疗的基础 目标。在此提案中，我们将开发自定义的计算方法，并进行综合多数 OMICS分析的目的是发现射击剪接，驾驶员出口剪接事件的调节和 癌症中肿瘤特异性出口的新抗原。 （目标1）我们将开发一个集成框架 通过多种测序生成的多摩学数据的联合分析来检测和验证出口 平台。我们将确定优先影响癌症出射接剪接的剪接因子。 （目标2）我们将 开发新的统计方法，以鉴定具有出脱键改变的基因和途径。 我们将实施一个半监督的机器学习模型，以预测出口剪接相关的癌症 基于转录组特征的驱动基因。 （目标3）我们将开发一个计算工具来识别剪接 - 衍生的新抗原并通过基于质谱的免疫肽组数据对其进行验证。我们将 评估接受剪接剪接的新抗原与接受的患者的临床结局的关联 免疫检查点抑制剂治疗。该项目将为专门提供独特的计算平台 出口剪接分析。从这项拟议的研究中获得的知识将有助于了解 出口改变促进癌症进展的基本机制。我们希望这些 分析将通过提供新方法来预测患者反应，将分析迅速转化为临床实用程序 免疫检查点抑制疗法。