Genomic targets of oncoproteins and tumor suppressors

描述(申请人提供)：功能基因组学的一个关键目标是确定整个人类基因组中具有生理意义的转录调控元件。尽管这一目标很重要，但不偏不倚的、全基因组范围的转录调控元件鉴定从未以实验方式描述过。许多转录调控蛋白通过作为癌蛋白和肿瘤抑制蛋白在人类癌症中发挥因果作用。染色质免疫沉淀(ChIP)和微阵列分析的结合是在全基因组水平上确定体内靶点的有力工具，这种方法通常揭示了对转录调节蛋白生物学功能的意想不到的和关键的见解。与Affymetrix的Tom Gingera合作，我们使用了代表所有人类21号和22号染色体的平铺微阵列，对p53肿瘤抑制因子、Myc癌蛋白和Sp1进行了无偏见和全面的芯片分析。这项分析已经确定了许多生理靶点，其中许多位于意想不到的基因组位置。我们打算继续这项合作，在全基因组范围内定义许多与人类癌症直接相关的转录调控蛋白的生理靶点。将使用两个实验系统。首先，我们将使用未转化的人乳腺上皮细胞系MCF-10A细胞，在该细胞系中，可以快速诱导Src癌基因(融合到雌激素受体的配体结合域)的表达，从而使“正常”细胞与转化途径上处于不同状态的细胞进行比较。其次，我们将分析T抗原+活化RAS转化的h-TERT永生化成纤维细胞。最初的实验将在ENCODE阵列上的大约50种蛋白质(包括P53、Jun-Fos、Myc、Myb、Ets、Rb、E2F癌蛋白家族和肿瘤抑制因子的多个成员)上进行，这些蛋白质占基因组的1%。根据这些芯片结果和基因表达谱，最感兴趣的大约35种蛋白质(平均1.5个条件/蛋白质)的目标位置将在全基因组的基础上使用本提案开始时应该可用的阵列来确定。这些结果将提供大量公正和全面的信息，如1)已知和未知基因的生理靶点位置，2)癌基因调控基因和特定因子之间的联系，3)调控基因上的因子占据是调控的还是非调控的，4)与癌调控基因优先相关的因子的特定组合，5)多蛋白家族中蛋白质结合的特异性和冗余性，6)调控回路的识别。在两种状态之间明确的转变过程中，对正常细胞和转化细胞进行详细的分子比较，对从事癌症许多不同方面工作的众多研究人员来说都应该非常有用。更广泛地说，这些结果应该代表了功能基因组学的一个新维度，它们应该催生各种实验和计算方法，以了解癌蛋白和肿瘤抑制蛋白是如何导致癌症的。

DESCRIPTION (provided by applicant): A key goal of functional genomics is to identify transcriptional regulatory elements throughout the entire human genome that are of physiological significance. Despite the importance of this goal, unbiased, genome-wide identification of transcriptional regulatory elements has never been described in an experimental fashion. Many transcriptional regulatory proteins play causal roles in human cancer by acting as oncoproteins and tumor suppressor proteins. The combination of chromatin immunoprecipitation (ChIP) and microarray analysis is a powerful tool for identifying in vivo target sites on a genome-wide level, and this approach has often revealed unexpected and crucial insights into the biological functions of transcriptional regulatory proteins. In collaboration with Tom Gingeras at Affymetrix, we have used tiled microarrays representing all of human chromosomes 21 and 22 to perform an unbiased and comprehensive ChIP analysis on the p53 tumor suppressor, the Myc oncoprotein, and Sp1. This analysis has identified numerous physiological targets, many of which are in unexpected genomic locations. We intend to continue this collaboration to define, on a whole-genome scale, physiological targets of many transcriptional regulatory proteins directly involved in human cancer. Two experimental systems will be employed. First, we will use MCF-10A cells, a non-transformed human breast epithelial cell line, in which expression of the Src oncogene (fused to the ligand-binding domain of estrogen receptor) can be rapidly induced, thereby permitting a comparison of "normal" cells with cells at different states along the transformation pathway. Second, we will analyze h-TERT immortalized fibroblasts that are or are not transformed with T antigen + activated Ras. The initial experiments will be performed on approximately 50 proteins (including multiple members of the p53, Jun-Fos, Myc, Myb, Ets, Rb, E2F families of oncoproteins and tumor suppressors) on the ENCODE array representing 1 % of the genome. From these ChIP results and gene expression profiles, target sites of approximately 35 proteins (average 1.5 conditions/protein) of highest interest will be determined on a whole-genome basis using arrays that should be available when this proposal would begin. The results will provide a massive amount oi unbiased and comprehensive information such as 1) the location of physiological target sites with respect to known and unknown genes, 2) links between oncogenically-regulated genes and specific factors, 3) whether factor occupancy at regulated genes is regulated or unregulated, 4) specific combinations of factors that preferentially associate with oncogenically-regulated genes, 5) the specificity and redundancy of binding by proteins in multiprotein families, 6) identification of regulatory circuits. This detailed molecular comparison of normal versus transformed cells during a defined transition between the two states should be extremely useful both to numerous investigators who work on many different aspects of cancer. More generally, the results should represent a new dimension in functional genomics, and they should spawn a variety of experimental and computational approaches to understand how oncoproteins and tumor suppressor proteins cause cancer.