Harnessing Rare Variants for Tumor Classification

摘要 这个项目涉及如何从突变中提取临床上可操作的信息用于诊断目的。 从肿瘤测序仪表板观察到的模式越来越多地用于常规医疗 癌症患者。近年来，人们对突变的格局进行了严格的审查，公开使用了 现有的数据库，如癌症基因组图谱和其他有关体细胞的重要信息来源 突变。然而，大部分的注意力都集中在主要的癌症基因上，特别是热点。 这些基因中经常发生突变的突变。然而，绝大多数体细胞突变 发生在“罕见”的遗传位点上。在10,295例TCGA肿瘤中观察到的1,788,153个不同的突变 超过92%是单基因突变，即只在一个肿瘤中观察到突变。此外，当新的肿瘤被 测序后，平均60%的突变是在TCGA中没有观察到的突变。到目前为止 调查人员大多忽略了这座隐藏的潜在信息的冰山。我们的建议的动机是 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 用于临床用途。在前期工作中，我们采用了统计方法，用于 其他科学领域的类似调查，如生态学和语言学中的物种鉴定 处理，并已经能够证明观察到已知稀有变异的概率 癌症基因因基因不同而明显不同，这些概率可以被准确地估计，而且对某些癌症基因来说 基因概率表现出很强的世系依赖性。受这些发现的启发，我们建议扩大 这些方法的范围是调查整个基因组的血统依赖关系，并使用 开发按组织起源部位对肿瘤进行分类的准确工具的信息。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 基因组根据其局部GC含量、DNA复制时间、转录活性、染色质 可获得性和相应起源组织中的组蛋白修饰标记，以期绘制地图 罕见和以前未观察到的变异的世系相关变异。在目标2中，我们将使用此信息 基于惩罚性分层混合效应统计模型构建分类工具 直接使用这些“元特征”来归因于罕见和以前看不到的歧视性影响 变种。我们将使用经验验证数据集检查模型的预测精度，并研究其 在不同数据设置的情况下的计算可行性，例如面板测序与整个外显子组 和全基因组。最终目标是创建一种工具，用于对起源的解剖部位进行分类 未知原发癌症和通过筛查血液中循环的肿瘤DNA而发现的癌症。

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.