Harnessing Rare Variants for Tumor Classification

利用罕见变异进行肿瘤分类

• 批准号: 10206386
• 负责人: Colin B Begg
• 金额: $ 40.49万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10206386
• 关键词: Anatomy; Attention; Belief; Blood; Blood Screening; Blood specimen; Cancer Patient; Chromatin; Classification; Clinical; Computational Linguistics; Computing Methodologies; DNA; DNA Replication Timing; Data; Data Set; Databases; Dependence; Diagnosis; Diagnostic; Ecology; Encyclopedia of DNA Elements; Epigenetic Process; Exhibits; Genes; Genetic Transcription; Genome; Genomics; Genotype; Goals; Guanine + Cytosine Composition; Iceberg; International; Investigation; Knowledge; Laboratories; Linguistics; Location; Malignant Neoplasms; Maps; Medical; Methods; Modeling; Modernization; Mutation; Oncogenes; Organ; Patients; Pattern; Probability; Research; Research Personnel; Signal Transduction; Site; Somatic Mutation; Source; Statistical Methods; Statistical Models; Techniques; Testing; The Cancer Genome Atlas; Tissues; Tumor Tissue; Untranslated RNA; Validation; Variant; Work; base; bioinformatics resource; cancer care; cancer genome; cancer site; cancer type; circulating DNA; classification algorithm; clinical application; clinically actionable; clinically relevant; exome; genomic locus; histone modification; individual patient; insight; language processing; novel; predictive tools; prototype; rare variant; screening; tool; tumor; tumor DNA; whole genome

Abstract This project concerns how to extract clinically actionable information for diagnostic purposes from mutational patterns observed from tumor sequencing panels that are increasingly being used in routine medical care of cancer patients. In recent years there has been intense scrutiny of the mutational landscape, using publicly available databases such as The Cancer Genome Atlas and other important sources of information on somatic mutations. However, the bulk of the attention has focused on major cancer genes, and especially the hotspot mutations in these genes at which mutations occur frequently. However, the vast majority of somatic mutations occur at “rare” genetic loci. Of the 1,788,153 distinct mutations that were observed in the 10,295 TCGA tumors over 92% were singletons, i.e. mutations observed in only one tumor. Moreover, when new tumors are sequenced, on average 60% of mutations observed are mutations that were not observed in TCGA. To date investigators have mostly ignored this “hidden iceberg” of potential information. Our proposal is motivated by the belief that at least a portion of these rare mutations contain important information that could be harnessed for clinical purposes. In preliminary work we have adapted statistical methods that were developed for use in analogous investigations in other scientific fields, such as species identification in ecology and language processing, and have been able to demonstrate that the probabilities of observing rare variants in known cancer genes differs markedly by gene, that these probabilities can be estimated accurately, and that for some genes the probabilities exhibit strong lineage dependency. Motivated by these findings, we propose to broaden the scope of these methods to investigate lineage dependency throughout the genome and to use the information to develop accurate tools for classifying tumors by tissue site of origin. In Aim 1, we will integrate data from various bioinformatic resources to characterize genes as well as mutations in non-coding parts of the genome on the basis of their local GC content, DNA replication timing, transcriptional activity, chromatin accessibility, and histone modification marks in the corresponding tissues-of-origin with a view to mapping lineage-dependent variation in rare and previously unobserved variants. In Aim 2, we will use this information to construct a classification tool based on a penalized hierarchical mixed-effects statistical model that permits direct use of these “meta-features” for imputing the discriminatory effects of rare and previously unseen variants. We will examine the predictive accuracy of the model using empirical validation datasets and study its computational feasibility in the context of different data settings, e.g. panel sequencing versus whole-exome and whole-genome. The ultimate goal is to create a tool for the classification of the anatomic site of origin of cancers of unknown primary and of cancers detected through screening of circulating tumor DNA in the blood.

抽象的 该项目涉及如何从突变中提取临床可行的信息以进行诊断目的 从肿瘤测序面板中观察到的模式，这些图案越来越多地用于常规医疗护理 癌症患者。近年来，人们对突变格局进行了严格的审查，并使用公开审查 可用的数据库，例如癌症基因组图集和其他有关体细胞的重要信息来源 突变。但是，大部分注意都集中在主要的癌症基因上，尤其是热点 突变经常发生的这些基因中的突变。但是，绝大多数躯体突变 发生在“稀有”遗传基因座。在10,295个TCGA肿瘤中观察到的1,788,153个不同的突变中 超过92％的单例，即仅在一个肿瘤中观察到的突变。而且，当新肿瘤是 测序，平均观察到的突变中有60％是在TCGA中未观察到的突变。迄今为止 调查人员主要忽略了潜在信息的“隐藏冰山”。我们的建议是由 相信这些罕见突变中至少一部分包含可以利用的重要信息 出于临床目的。在初步工作中，我们采用了用于使用的统计方法 其他科学领域的类似研究，例如生态和语言的规范 处理，并能够证明在已知中观察稀有变体的可能性 癌症基因的基因明显不同，可以准确地估算这些可能性，并且对于某些可能性 基因的可能性表现出强烈的谱系依赖性。在这些发现的激励下，我们建议扩大 这些方法的范围是研究整个基因组中的谱系依赖性并使用 信息以开发准确的工具，以通过组织部位对肿瘤进行分类。在AIM 1中，我们将整合 来自各种生物信息学资源的数据来表征基因以及在非编码部分的突变 基因组基于其本地GC含量，DNA复制时间，转录活性，染色质 可访问性和相应组织中的Hisstone修饰标记，以映射 稀有和以前未观察到的变体的谱系依赖性变化。在AIM 2中，我们将使用此信息 构建基于惩罚的层次混合效应统计模型的分类工具，该模型允许 直接使用这些“元功能”来推出罕见和以前看不见的歧视性效果 变体。我们将使用经验验证数据集检查模型的预测准确性，并研究其 在不同数据设置的上下文中的计算可行性，例如面板测序与整个外观 和整个基因组。最终目标是创建一种工具，以分类的分类。 通过筛选血液中循环肿瘤DNA检测到的未知原发性和癌症的癌症。