Harnessing Rare Variants for Tumor Classification

摘要 该项目涉及如何从突变中提取临床可操作的信息用于诊断目的。 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护理， 癌症患者。近年来，人们对突变景观进行了严格的审查， 现有的数据库，如癌症基因组图谱和其他重要的信息来源，对体细胞癌的研究。 突变。然而，大部分的注意力都集中在主要的癌症基因，特别是热点 这些基因中的突变频繁发生。然而，绝大多数的体细胞突变 发生在“罕见”的基因位点。在10，295例TCGA肿瘤中观察到的1，788，153个不同突变中， 超过92%是单例，即仅在一个肿瘤中观察到突变。此外，当新肿瘤 测序后，平均60%观察到的突变是TCGA中未观察到的突变。迄今 调查人员大多忽视了这一潜在信息的“隐藏冰山”。我们的提议是出于 相信这些罕见的突变中至少有一部分包含可以利用的重要信息， 用于临床目的。在初步工作中，我们已经采用了统计方法，这些方法是为了在 其他科学领域的类似研究，如生态学和语言中的物种识别 处理，并已能够证明，观察到罕见的变异，在已知的概率 癌症基因因基因而显着不同，这些概率可以准确估计，并且对于一些 基因的概率表现出很强的谱系依赖性。基于这些发现，我们建议扩大 这些方法的范围，以调查整个基因组的谱系依赖性，并使用 这些信息有助于开发准确的工具，用于根据组织来源部位对肿瘤进行分类。在目标1中，我们将整合 来自各种生物信息学资源的数据，以表征基因以及非编码部分的突变， 基因组的基础上，其本地GC含量，DNA复制时间，转录活性，染色质 可及性和相应组织中的组蛋白修饰标记，以便进行映射 罕见且以前未观察到的变体中的谱系依赖性变异。在目标2中，我们将使用这些信息 构建一个基于惩罚分层混合效应统计模型的分类工具， 直接使用这些“元特征”来估算罕见和以前看不见的歧视性影响， 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 不同数据设置背景下的计算可行性，例如面板测序与全外显子组 和全基因组最终的目标是建立一个工具，用于分类的解剖部位的起源， 原发性未知的癌症和通过筛选血液中的循环肿瘤DNA检测到的癌症。

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.