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.

项目总结及摘要 测序技术和计算算法的进步揭示了各种选择性剪接 癌症转录组的变异。尽管几种常见的剪接事件，例如外显子 跳跃、内含子保留和选择性剪接位点与肿瘤进展和治疗有关 由于缺乏抗性，许多非规范剪接事件在癌症中的作用仍然未知。 专门的方法来检测和表征这些事件。该提案将重点关注退出电子拼接 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 癌症相关基因。退出子是编码外显子内的内部区域，具有剪接潜力以产生 一个神秘的内含子。退出子的剪接会导致蛋白质亚型的序列发生改变，从而可能影响功能 域和翻译后修饰位点。癌症中发生退出子剪接的观察 基因表明，退出子剪接异构体可能有助于癌症的发展。此外，肿瘤—— 导致内部缺失或移码的特定退出子剪接连接可能产生免疫原性 肽（即新抗原）可成为开发癌症疫苗或 T 细胞治疗药物的基础 目标。在这个提案中，我们将开发定制的计算方法并进行综合多元 组学分析，旨在揭示退出子剪接的调控、驱动退出子剪接事件和 来自癌症中肿瘤特异性退出子的新抗原。 （目标1）我们将开发一个综合框架 通过联合分析多重测序生成的多组学数据来检测和验证退出子 平台。我们将确定优先影响癌症中退出子剪接的剪接因子。 （目标2）我们将 开发新的统计方法来识别富含退出子剪接改变的基因和通路。 我们将实施半监督机器学习模型来预测与退出子剪接相关的癌症 基于转录组特征的驱动基因。 （目标 3）我们将开发一种计算工具来识别剪接- 衍生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 评估退出电子剪接衍生的新抗原与接受治疗的患者的临床结果之间的关联 免疫检查点抑制剂治疗。该项目将为专用的计算平台提供一个独特的计算平台。 退出子剪接分析。从这项拟议的研究中获得的知识将有助于理解 退出子改变促进癌症进展的潜在机制。我们期望这些 通过提供预测患者反应的新方法，分析将迅速转化为临床实用性 免疫检查点抑制疗法。