Heterogeneous Cancer Progression from Microarray Data

描述(申请人提供)：来自微阵列数据的异质性癌症进展从原则上讲，这类统称为癌症的疾病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”，每一种类型都有一种共同的方式，即控制细胞生长的功能被禁用。通过识别这些常见的亚型和产生它们的特定遗传异常序列，我们可以识别可能对与普通人群不同的治疗反应的患者亚群，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。由于不同的癌症亚型具有过度活跃或过度不活跃基因的特征模式，在检测肿瘤内基因表达方面取得了很大进展。然而，试图解释这些表情数据是一个困难的问题，对于这个问题，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--为解释不同细胞类型在肿瘤内进化的可能途径提供了一种强有力的方法。这种系统进化方法有两个重要的变种：一个使用从基因表达微阵列收集的数据，分析大肿瘤样本中的平均数千个基因；另一个使用从细胞学研究收集的数据，分析从肿瘤分离出来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，后者通过识别在个别肿瘤中共同出现的细胞类型，提供关于肿瘤进化的有价值的线索。拟议的工作将为这些问题开发新的计算机模型，以便开发一种具有两种方法优点的单一方法。这项工作将首先开发一种方法，从患者群体中抽样的肿瘤的大量微阵列测量中推断常见细胞类型的存在。然后，它将建立在先前方法的基础上，推断这些肿瘤状态之间的进化相似性。最后，它将使细胞学肿瘤系统发育的方法适用于从这些微阵列状态推断进化序列的问题。其结果将是一种推断单个细胞状态进化的统一方法，就像在细胞学研究中那样，但在数千个基因上进行分析，就像在微阵列研究中一样。统一的方法将在乳腺癌数据上得到验证，对于这些数据，微阵列和细胞学测量都可用，并应用于发现乳腺癌人群中的常见进展路径。这项研究有望揭示乳腺癌进展的不同阶段，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。待开发的方法很可能对实体瘤进展以及分析混合样本中细胞分化的相关问题具有更广泛的适用性。

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.