Mutational signature analysis: methods and applications to the clinic

突变特征分析：方法和临床应用

• 批准号: 10418967
• 负责人: Peter J Park
• 金额: $ 45.32万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-05 至 2027-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10418967
• 关键词: Adopted; Algorithms; Attention; Biological; Biological Assay; Biological Markers; Biological Process; Cancer Patient; Catalogs; Cells; Characteristics; Classification; Clinic; Clinical; Clinical Data; Collaborations; Communities; Computing Methodologies; Cytidine Deaminase; DNA; DNA Double Strand Break; DNA Sequence Alteration; DNA sequencing; Data; Data Set; Detection; Development; Disease; Ensure; Evolution; Formulation; Genes; Genome; Genomic Instability; Goals; Immune; Immunotherapy; Lead; Malignant Neoplasms; Methodology; Methods; Mismatch Repair; Mismatch Repair Deficiency; Modeling; Monitoring for Recurrence; Mutagenesis; Mutation; Mutation Detection; Patients; Pattern; Point Mutation; Process; Refit; Sampling; Screening for cancer; Selection for Treatments; Sensitivity and Specificity; Single base substitution; Somatic Mutation; Source; System; Techniques; Variant; Work; base; brca gene; clinical application; clinical care; cohort; data standards; detection method; exome; exome sequencing; gene panel; genome sequencing; homologous recombination; immune checkpoint blockade; improved; inhibitor; insertion/deletion mutation; neoantigens; novel; patient biomarkers; patient stratification; patient subsets; predict clinical outcome; predictive modeling; profiles in patients; repaired; replication stress; resistance mechanism; responders and non-responders; response; sequencing platform; simulation; single-cell RNA sequencing; tool; transcriptomics; tumor; tumor DNA; tumor heterogeneity; whole genome

PROJECT SUMMARY Mutational signature analysis is a recent analytical approach for interpreting somatic mutations in the genome. It utilizes the sequence context of point mutations as well as the size and type of copy number and structural aberrations to decompose the observed mutational patterns into distinct 'signatures', some of which have been associated with specific biological processes. In this project, we will develop more robust and sensitive com- putational methods for mutational signature analysis and apply them to several clinically important questions. Our initial work has attracted a great deal of attention from clinicians, and we will analyze data from several clinical cohorts in close collaboration. In Aim 1, we will build upon our previous work to devise a method that can identify homologous recombination deficiency in cancer patients profiled on gene panels. Although whole-genome sequencing offers several advantages, gene panel sequencing remains as a pivotal platform in clinical care. Our method will enable signature analysis for gene panels from which only a small number of mutations can be observed. We will incorporate additional sources of information and identify biomarkers for patient stratification. In Aim 2, we will investigate other types of genomic instability such as mismatch repair deficiency, replication stress, and APOBEC mutagenesis. For example, although patients with mismatch repair deficiency generally respond better to immunotherapy, there is a considerable variation across patients. We will use mutational signatures to identify a subset of patients that respond better. In Aim 3, we will extend our method to data from circulating tumor DNA and single cell RNA sequencing to enable signature-based predictive modelling. In Aim 4, we will develop a generalized analytical framework for whole-genome and whole-exome signature analysis that will overcome the shortcomings of current approaches. We will use this new framework to build a high-quality reference catalog for the community.

项目概要 突变特征分析是解释基因组体细胞突变的最新分析方法。 它利用点突变的序列背景以及拷贝数和结构的大小和类型 畸变将观察到的突变模式分解为不同的“特征”，其中一些已被 与特定的生物过程相关。在这个项目中，我们将开发更强大、更敏感的组件 突变特征分析的推定方法并将其应用于几个临床重要问题。 我们的初步工作引起了临床医生的极大关注，我们将分析几个方面的数据 临床队列密切合作。在目标 1 中，我们将在之前的工作基础上设计一种方法 可以识别基因组上癌症患者的同源重组缺陷。虽然 全基因组测序具有多种优势，基因组测序仍然是一个关键平台 临床护理。我们的方法将能够对基因组进行特征分析，其中只有一小部分 可以观察到突变。我们将纳入额外的信息来源并确定生物标志物 患者分层。在目标 2 中，我们将研究其他类型的基因组不稳定性，例如错配修复 缺陷、复制压力和 APOBEC 突变。例如，虽然患有错配修复的患者 缺乏症通常对免疫疗法有更好的反应，但患者之间存在很大差异。我们 将使用突变特征来识别反应更好的患者子集。在目标 3 中，我们将扩展我们的 方法从循环肿瘤 DNA 和单细胞 RNA 测序中获取数据，以实现基于特征的预测 造型。在目标 4 中，我们将开发一个全基因组和全外显子组的通用分析框架 签名分析将克服当前方法的缺点。我们将使用这个新框架 为社区建立高质量的参考目录。