Heterogeneous Cancer Progression from Microarray Data

描述（由申请人提供）：来自微阵列数据的异质性癌症进展，统称为癌症的一类疾病原则上可以由无限数量的突变组合产生。尽管如此，很明显，大多数癌症可以分为几种常见的“亚型”，每种亚型的特征都是细胞生长控制失效的共同方式。通过识别这些常见的亚型和产生它们的遗传异常的特定序列，我们可以识别可能对与普通人群不同的治疗产生反应的患者亚群，找到可能成为新抗癌药物有用靶点的基因，并开发诊断测试以更好地预测患者结果并建议哪些药物将使哪些患者受益。通过检查肿瘤内的基因表达已经取得了很大的进展，因为不同的癌症亚型具有过度活跃或过度失活基因的特征模式。然而，试图解释这些表达数据是一个困难的问题，复杂的计算机模型已被证明是无价的。一类计算机模型-系统发育（进化树）模型-提供了一种强大的方法来解释不同细胞类型在肿瘤中进化的可能途径。这种系统发育方法有两种重要的变体：一种使用从基因表达微阵列收集的数据，其分析了大肿瘤样本中平均数千个基因，另一种使用从细胞计数研究收集的数据，其分析了从肿瘤中分离的单个细胞中的少量基因。每种方法都有优势，前者可以更全面地了解整体基因活性，后者可以通过识别个体肿瘤中共同出现的细胞类型来提供有关肿瘤演变的有价值的线索。拟议的工作将开发新的计算机模型，这些问题，以开发一个单一的方法，这两种方法的优点。这项工作将首先开发方法，从患者人群中肿瘤样本的批量微阵列测量中推断常见细胞类型的存在。然后，它将建立在以前的方法，以推断这些肿瘤状态之间的进化相似性。最后，它将使细胞计数肿瘤微阵列遗传学的方法适应于从这些微阵列状态推断进化序列的问题。其结果将是一个统一的方法来推断个体细胞状态之间的进化，就像在细胞计数研究中一样，但在数千个基因上进行检测，就像在微阵列研究中一样。统一的方法将在乳腺癌数据上进行验证，其中微阵列和细胞计数测量都可用，并应用于发现乳腺癌人群中的共同进展途径。该研究有望揭示乳腺癌进展中的不同阶段，这些阶段在现有方法中并不明显，有助于识别新的患者亚群，药物靶点和诊断测试。待开发的方法可能具有更广泛的适用性，一般的实体瘤的进展，并分析混合样品中的细胞分化的相关问题。

DESCRIPTION (provided by applicant): Heterogeneous Cancer Progression from Microarray Data the class of diseases collectively known as cancer could in principle be produced by a limitless number of combinations of mutations. Nonetheless, it has become apparent that most cancers can be grouped into a few common "sub-types," each characterized by a common way in which the controls on cell growth become disabled. By identifying these common sub-types and the particular sequences of genetic abnormalities that produce them, we can identify patient sub-populations who may respond to different treatments than the general population, find genes that may be useful targets for new anti-cancer drugs, and develop diagnostic tests to better predict patient outcomes and suggest which drugs will benefit which patients. Great progress has been made by examining gene expression within tumors, as different cancer sub-types have characteristic patterns of overly active or overly inactive genes. Trying to interpret these expression data is, however, a difficult problem for which sophisticated computer models have proven invaluable. One class of computer models - phylogenetic (evolutionary tree) models - has provided a powerful method for interpreting likely pathways by which different cell types evolve within tumors. There are two important variants of this phylogenetic approach: one using data gathered from gene expression microarrays, which assay thousands of genes averaged over large tumor samples, and another using data gathered from cytometric studies, which assay small numbers of genes in individual cells isolated from tumors. Each has advantages, the former in allowing a far more complete picture of overall gene activity and the latter in providing valuable clues about tumor evolution by identifying which cell types co-occur in individual tumors. The proposed work will develop new computer models for these problems in order to develop a single approach with the advantages of both methods. The work will first develop approaches to infer the existence of common cell types from bulk microarray measurements of tumors sampled across patient populations. It will then build on prior methods to infer evolutionary similarity between these tumor states. It will, finally, adapt methods for cytometric tumor phylogenetics to the problem of inferring evolutionary sequences from these microarray states. The result will be a unified approach for inferring evolution among individual cell states, as in a cytometric study, but assayed on thousands of genes, as in a microarray study. The unified approach will be validated on breast cancer data, for which both microarray and cytometric measurements are available, and applied to the discovery of common progression pathways in breast cancer populations. The study can be expected to uncover distinct stages in the breast cancer progression that would not be apparent by existing methods, aiding in the identification of new patient sub-populations, drug targets, and diagnostic tests. The methods to be developed are likely to have broader applicability to solid tumor progression in general and to related problems of analyzing cell differentiation in mixed samples.