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用于诊断目的的临床可操作信息 从肿瘤测序面板中观察到的模式越来越多地用于常规医疗保健 近年来，人们对突变情况进行了严格的审查，并公开使用。 可用的数据库，例如癌症基因组图谱和其他重要的体细胞信息来源 然而，大部分注意力都集中在主要的癌症基因上，尤其是热点基因上。 这些突变经常发生，但绝大多数是体细胞突变。 在 10,295 个 TCGA 肿瘤中观察到的 1,788,153 个不同突变发生在“罕见”基因位点。 超过 92% 是单一肿瘤，即仅在一种肿瘤中观察到突变。 经过测序，平均 60% 观察到的突变是迄今为止 TCGA 中未观察到的突变。 调查人员大多忽略了这个潜在信息的“隐藏冰山”。我们的提议的动机是。 相信这些罕见突变中至少有一部分包含可以利用的重要信息 在初步工作中，我们采用了为临床目的而开发的统计方法。 其他科学领域的类似研究，例如生态学和语言中的物种识别 处理，并且已经能够证明在已知的情况下观察到罕见变异的概率 癌症基因因基因而异，这些概率可以准确估计，并且对于某些基因来说 受这些发现的启发，我们建议扩大基因的谱系依赖性。 这些方法的范围是研究整个基因组的谱系依赖性并使用 在目标 1 中，我们将整合开发根据组织来源对肿瘤进行分类的准确工具的信息。 来自各种生物信息资源的数据，用于表征基因以及非编码部分的突变 基因组基于其局部 GC 含量、DNA 复制时间、转录活性、染色质 相应来源组织中的可及性和组蛋白修饰标记，以进行绘图 罕见和以前未观察到的变异中的谱系依赖性变异 在目标 2 中，我们将使用此信息。 构建基于惩罚分层混合效应统计模型的分类工具，该模型允许 直接使用这些“元特征”来估算罕见和以前未见过的歧视性影响 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。 不同数据设置下的计算可行性，例如面板测序与全外显子组测序 最终目标是创建一种对起源的解剖部位进行分类的工具。 原发性不明的癌症和通过血液中循环肿瘤 DNA 筛查检测到的癌症。